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Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey.

Ida Nurcahyani^1,2, Jeong Woo Lee¹

¹School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea.

Sensors (Basel, Switzerland)

|October 13, 2021

Summary

Machine learning enhances vehicular network resource allocation by optimizing data traffic management. This survey analyzes AI-driven strategies for efficient resource utilization in smart vehicles.

Keywords:

machine learning resource allocation survey paper vehicular network

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Area of Science:

Computer Science
Electrical Engineering
Artificial Intelligence

Background:

Smart vehicles generate massive data traffic, straining limited vehicular network resources.
Artificial Intelligence (AI) offers potential for improved resource utilization and reliable services.

Purpose of the Study:

To survey and analyze the role of machine learning (ML) in vehicular network resource allocation.
To understand how ML algorithms dynamically manage and allocate network resources for smart vehicles.

Main Methods:

Analysis of ML-driven resource allocation scenarios in vehicular networks.
Classification of mechanisms based on chosen algorithmic parameters.
Identification of challenges in ML-based vehicular network resource allocation.

Main Results:

Identified diverse ML approaches for vehicular network resource management.
Classified strategies based on key parameters influencing algorithm design.
Highlighted critical challenges in implementing ML for dynamic resource allocation.

Conclusions:

ML is pivotal for dynamic resource allocation in vehicular networks.
Understanding ML's role is crucial for future smart vehicle network development.
Further research is needed to address identified challenges for robust implementation.